{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frCVavK9MrcM",
        "outputId": "45d00aee-c0f8-466c-8b55-8634afcf3d45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bztLVfgOckZ",
        "outputId": "4fd9c122-cf66-48cf-eb46-2dc280e89e4a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Each time restarting Colab, remember to reload Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gjZu_kJO0Tu",
        "outputId": "5b6bbc2b-1f7c-4ee8-a99c-0a4f27977beb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 1: Load Dataset\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Load the dataset\n",
        "file_path = '/content/drive/MyDrive/CISC7204/Assignment2/endometrial_cancer_dataset.xlsx'\n",
        "data = pd.read_excel(file_path)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "print(data.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2iIC4JDzOhg3",
        "outputId": "5e446a75-efb3-4c4f-b2fe-1aaced018c94"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  gene number         1         2         3         4         5         6  \\\n",
            "0      normal -1.103666  0.206776 -0.534131  0.603655 -2.441400 -0.995328   \n",
            "1      normal  0.410440 -0.134260 -0.308197  0.377501 -1.391914 -0.192229   \n",
            "2      normal -2.015880 -1.034622 -0.661186  0.097456 -0.714329  0.278067   \n",
            "3      normal  0.427657 -0.359115 -0.094442 -0.110739 -1.987891  0.660950   \n",
            "4      normal -0.085108 -0.034191 -0.421132  0.429815 -1.059151 -0.382955   \n",
            "\n",
            "          7         8         9  ...        41        42        43        44  \\\n",
            "0 -0.930380  0.318872 -1.675583  ...  0.296488  2.339507 -0.696031  0.002218   \n",
            "1  0.222256 -0.313873  0.089968  ... -0.023796  3.357155 -0.336294 -0.061789   \n",
            "2  1.573029 -0.191027 -2.817496  ... -0.337802 -0.628069 -2.378160 -1.657204   \n",
            "3 -0.351836 -0.191383 -0.691392  ...  0.338545  1.275352 -1.262034  0.195727   \n",
            "4 -0.694271  0.264023 -0.381253  ...  0.202938  2.748094 -0.002223  0.322189   \n",
            "\n",
            "         45        46        47        48        49        50  \n",
            "0 -0.096930  0.276814  0.038644  1.965618 -2.371782 -0.619440  \n",
            "1  0.302761  0.909447  0.016731  3.318433 -1.200479 -0.401531  \n",
            "2 -0.353883  0.403069 -0.210233  3.356462 -0.017995 -0.192662  \n",
            "3 -0.032908  0.893805  0.141939  3.950930 -0.525310  0.863726  \n",
            "4 -0.228213  0.342300 -0.036789  2.379835 -0.458716 -0.814524  \n",
            "\n",
            "[5 rows x 51 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 2: Data Preprocess\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Drop the first column if it is not needed for PCA\n",
        "data = data.drop(data.columns[0], axis=1)\n",
        "\n",
        "# Check for missing values\n",
        "print(data.isnull().sum())\n",
        "\n",
        "# Fill missing values with the mean of each column\n",
        "data = data.fillna(data.mean())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hFtlpwjPP5J",
        "outputId": "f8ccb5e3-3af3-4522-cee6-a1bf603e3705"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1     0\n",
            "2     0\n",
            "3     1\n",
            "4     0\n",
            "5     0\n",
            "6     0\n",
            "7     0\n",
            "8     0\n",
            "9     0\n",
            "10    0\n",
            "11    0\n",
            "12    1\n",
            "13    0\n",
            "14    0\n",
            "15    0\n",
            "16    0\n",
            "17    0\n",
            "18    0\n",
            "19    0\n",
            "20    0\n",
            "21    0\n",
            "22    0\n",
            "23    0\n",
            "24    0\n",
            "25    0\n",
            "26    0\n",
            "27    0\n",
            "28    0\n",
            "29    2\n",
            "30    0\n",
            "31    0\n",
            "32    0\n",
            "33    0\n",
            "34    0\n",
            "35    0\n",
            "36    0\n",
            "37    0\n",
            "38    0\n",
            "39    0\n",
            "40    0\n",
            "41    0\n",
            "42    0\n",
            "43    0\n",
            "44    0\n",
            "45    0\n",
            "46    0\n",
            "47    0\n",
            "48    1\n",
            "49    0\n",
            "50    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 3: Standardize the data\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "scaler = StandardScaler()\n",
        "data_scaled = scaler.fit_transform(data)"
      ],
      "metadata": {
        "id": "T6844ovsPXTw"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 4: Begin the analysis\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Perform PCA with two components\n",
        "pca = PCA(n_components=2)\n",
        "principal_components = pca.fit_transform(data_scaled)\n",
        "\n",
        "# Create a DataFrame with the principal components\n",
        "pca_df = pd.DataFrame(data=principal_components, columns=['Principal Component 1', 'Principal Component 2'])\n",
        "\n",
        "# Display the explained variance ratio\n",
        "print(pca.explained_variance_ratio_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONKJMCp7P0Hh",
        "outputId": "0d20adea-69ab-475b-a77c-d583a61d0afb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.16624545 0.13048126]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 5: Draw the map\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Plot the PCA scatter plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(pca_df['Principal Component 1'], pca_df['Principal Component 2'], alpha=0.5)\n",
        "plt.title('PCA Scatter Plot')\n",
        "plt.xlabel('Principal Component 1')\n",
        "plt.ylabel('Principal Component 2')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Om6ku7XPP-OO",
        "outputId": "a060f098-37b5-462b-f4ce-b3860853fabe"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 6: Give an explanation\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Calculate the total variance explained by the first two principal components\n",
        "total_variance_explained = sum(pca.explained_variance_ratio_)\n",
        "print(f'Total variance explained by the first two principal components: {total_variance_explained:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyn9_miSQKYP",
        "outputId": "bef02a50-07ba-4765-f7f3-fe4db3713c4e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total variance explained by the first two principal components: 0.30\n"
          ]
        }
      ]
    }
  ]
}